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FORONE

“A diet management application for healthy eating habits of single-person households in their 20s”

Members

Moon Kiwon / Lee Soui / Kim Sooyeon / Jeon Jongseok

1. Introduction

background

Single-person households are households that make a living by cooking and sleeping in independent spaces alone, and according to the Ministry of Public Administration and Security’s resident registration demographics, the proportion of single-person households is increasing every year from 34.9% in 2016 to 39.24% in 2020. Among them, the proportion of young people in their 20s and 30s living alone, especially for school and work, increased 1.26 times in four years from 887,000 in 2015 to 1.18 million in 2019.

As the number of single-person households increases, more and more studies are being done on food consumption and food expenditure patterns of single-person households, and the results show that single-person households have different characteristics and consequent problems.

  1. Single-person households have shorter meal-related hours except for snacks and drinks compared to multi-person households, and irregular meal times, with the lowest breakfast rate.

  2. When you eat alone, you will have incorrect eating habits, such as fasting, eating out, nutritional imbalance, and the possibility of obesity. The percentage of single-person households eating alone is 90%, more than four times higher than that of multi-person households, which is about 20%.The word ’family meals’ is not just about family eating together, but they have an important impact on family members’ formation and maintenance of proper eating habits by encountering various foods together and providing balanced meals, and the biggest problem when eating alone is poor eating.

It can also be expected that people in their 20s prefer consumption of instant or simple foods for convenience and economic reasons, and that nutrition is also not balanced.

We found out through background research that single-person households in their 20s have problems with their eating habits compared to other households, and created a health care app to solve them.

target user

The app’s target users are one-person households in their 20s. According to statistics such as diet habits by household type, age, or nutrient intake, single-person households in their 20s had the most irregular diet and uneven nutrition intake. For these reasons, we want to help single households in their 20s interact with healthcare apps to have the right eating habits, improve their eating habits in the long run, and lead a healthy life.

Goal

The ForOne app provides information on proper eating habits and diets for single-person households in 20s with disproportionate diets and helps with continued use.

Users can analyze and manage their diets through photographs rather than through them. When the application analyzes the calories and nutrients in the food and informs users of their eating habits, users can correct their eating habits and take care of their health according to the analysis.

In addition, since food habits are not formed in a short period of time, we motivate users to eat the right meals by providing notification services so that people can use the application consistently.

2. Main contents

System architecture

Main Function

  1. Login(User information and goal of App) Choose the user’s information (name, height, weight) and the purpose of using the app (weight retention, weight loss, weight gain).

  1. Food recognition When a user takes a picture of a food or enters a picture of the food, the user recognizes the food. Food recognition uses label detection from the Google Cloud Vision API. If you don’t recognize food properly with pictures, you can type the name of the food in text.

This is a code that uses Google cloud vision api to recognize the user-selected photos in a photo file and to read description and scores of the food labels.

os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'my first API-2020dd811d20.json'
def openFile():
  global filename
  filename = askopenfilename(title='카메라', filetypes=(("모든 파일", "*.*"), ("GIF파일", "*.gif")))
  photo = Image.open(filename)
  photo = photo.resize((170, 170))
  ph = ImageTk.PhotoImage(photo)
  imglabel1 = tk.Label(self, image=ph)
  imglabel1.image = ph
  imglabel1.place(x=115, y=50)
  print(filename)
  client = vision.ImageAnnotatorClient()
  #이미지 읽기
  with io.open(filename, 'rb') as image_file:
      content = image_file.read()
  image = types.Image()
  image = vision.Image(content=content)
  response = client.label_detection(image=image)
for label in response.label_annotations:
   global food
  global foodscore
  print("Label: ", label.description, "/ score: ", label.score)
  if label.description in foodlist:
    
      food = label.description
      foodscore = round(label.score*100, 1)
      print(food)
      print(label.score)
      print(foodscore)
  1. Calculating Calories When a user enters food and quantity, it calculates the calories consumed by the user based on the food and amount recognized. Calories are shown to be the same as the calories consumed in the meal and the accumulated calories per day.

This is a code that takes the name of the food and returns calories to update the total calories.

label2.configure(text='점심: ' + food2.get() + ' 360kcal (한 조각)')
totalCal += 360 * float(howDish2)
st.delete(1.0, END)
st.insert(END, '섭취 칼로리는 ' + str(totalCal) + 'kcal 입니다')
  1. Past Report It shows how many calories users have consumed in the past. In addition, the target calories are set differently depending on the goals set by the user when logging in, showing them as the calories consumed by the user.

  1. Notification It is a function that helps users not forget to input food. If there is anything that has not been registered, the notification allows the user to enter the food without forgetting it.

We build a Python GUI Application with Tkinter in this project, and this is a code that implements destroy and notification windows popping up through the “try finally” syntax.

def restart():
  global self
  try:
      if ('normal' == self.state()):
          self.destroy()
  finally:
alarm = tk.Tk()
alarm.title("For one Alarm")
alarm['bg'] = '#FFFACD'
alarm.geometry("200x200+500+300")
label2 = Label(alarm, text='Meal Time!', width=120, height=150, fg='red', background='#FFFACD',
             bitmap='info', compound='top', font=45)
label2.pack()
#label2.place(x=50,y=50)
time.sleep(1)
self.mainloop()

Demo video

3. Conclusion

Experimental methods, experimental results.

Experimental Methods, Subjects

Experimental methods: Experimental methods were conducted in the form of face-to-face interviews. The subjects were students in their early to mid-20s at Handong University, who currently live in dormitories or live outside and have problems managing their diet.

The experiment was conducted when the moderator ordered the user. Quantitative data were measured during the experiment, such as the time taken by the user, hesitation, etc., and qualitative data was obtained as numerical data through interviews with the user after the experiment.

Data

Result

Interpreting Data

Insight

Problems and Directions

  1. Problems and improvement plans
    • Full Design/UI Reconfiguration As a whole, problems with design and UI have been raised. - Easy to create and use data stores. It is necessary to create a data store, store user’s data, and make it easier to utilize.
    • Increase awareness with a variety of data. Through various data, I realized that recognition rate should be increased. - Add sound or graphics to alerts to help users understand better. Sound and graphics should make it easier for users to understand. - Process and provide information that users can easily understand and utilize. (e.g., showing calories, what exercise you need to do to lose weight, and what problems you may have if you lack the nutrients) Nutrition information and calorie information can be difficult to understand for users. Information should be processed and provided to users for easy understanding and utilization.
  2. Future Developments Directions
    • User friendly and fashionable UI
    • Provides high profile through extensive data
    • Provides information that is easy for users to understand and use.

4. Reference

Statistics